Directory-Aware Query and Maintenance in Vector Databases

2026-06-15

AI Summary

Traditional vector databases treat metadata as flat scalar attributes, which inadequately captures hierarchical directory semantics, leading to inefficient range queries, high overhead for structural updates, and challenges in maintaining consistency. This work introduces directory semantics as a first-class feature in vector databases, proposing Directory Semantic Query (DSQ) and Directory Semantic Maintenance (DSM) operations. To preserve directory topology and avoid the latency and write amplification caused by path expansion, we design TrieHI, a Trie-based hierarchical index that enables efficient recursive retrieval and low-cost structural modifications. Extensive experiments on ByteDance’s Viking engine demonstrate the superiority of our approach. We also release two large-scale datasets, WIKI-Dir and ARXIV-Dir, and have integrated TrieHI into OpenViking, an open-source context database for AI agents.

Abstract

Vector databases typically manage metadata as flat scalar attributes, which limits their ability to express hierarchical directory semantics commonly used to organize code repositories, enterprise documents, and agent memories. As a result, directory-scoped retrieval and structural updates are often implemented as application-layer workarounds, making recursive scope resolution expensive and directory maintenance difficult to keep consistent. This paper studies native directory semantics as a first-class capability for vector databases. We formalize two core operators: Directory-Semantic Query (DSQ) for hierarchically scoped retrieval, and Directory-Semantic Maintenance (DSM) for structural updates. We then evaluate three implementation strategies: query-time path expansion (PE-Online), ingestion-time path expansion (PE-Offline), and a Trie-based Hierarchical Index (TrieHI). Our analysis exposes the fundamental limitations of expansion-based designs: flattening the hierarchy incurs high recursive-query latency in PE-Online and unscalable write amplification during structural changes in both expansion strategies. In contrast, TrieHI keeps the directory topology as a native prefix tree, enabling efficient recursive retrieval through tree traversal and reducing maintenance cost through topological node manipulation. We benchmark these design points within ByteDance's Viking vector search engine and release two large-scale datasets, WIKI-Dir and ARXIV-Dir, to support future research on directory-semantic vector search. Finally, TrieHI has been integrated into OpenViking, an open-source context database for AI agents, where it supports filesystem-style context organization and directory-recursive retrieval.

Problem

• vector databases
• directory semantics
• hierarchical metadata
• recursive retrieval
• structural updates

Innovation

• Directory-Semantic Query
• Directory-Semantic Maintenance
• Trie-based Hierarchical Index
• Vector Database
• Hierarchical Retrieval